Overview

Dataset statistics

Number of variables25
Number of observations99976
Missing cells227059
Missing cells (%)9.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.1 MiB
Average record size in memory200.0 B

Variable types

Numeric23
Categorical2

Alerts

account_amount_added_12_24m is highly correlated with num_unpaid_bills and 2 other fieldsHigh correlation
account_days_in_rem_12_24m is highly correlated with sum_capital_paid_account_12_24mHigh correlation
account_incoming_debt_vs_paid_0_24m is highly correlated with num_unpaid_billsHigh correlation
avg_payment_span_0_12m is highly correlated with avg_payment_span_0_3m and 1 other fieldsHigh correlation
avg_payment_span_0_3m is highly correlated with avg_payment_span_0_12mHigh correlation
max_paid_inv_0_12m is highly correlated with max_paid_inv_0_24m and 2 other fieldsHigh correlation
max_paid_inv_0_24m is highly correlated with max_paid_inv_0_12m and 3 other fieldsHigh correlation
num_active_div_by_paid_inv_0_12m is highly correlated with num_active_inv and 1 other fieldsHigh correlation
num_active_inv is highly correlated with num_active_div_by_paid_inv_0_12m and 1 other fieldsHigh correlation
num_arch_ok_0_12m is highly correlated with max_paid_inv_0_12m and 3 other fieldsHigh correlation
num_arch_ok_12_24m is highly correlated with max_paid_inv_0_24m and 2 other fieldsHigh correlation
num_arch_rem_0_12m is highly correlated with avg_payment_span_0_12mHigh correlation
num_unpaid_bills is highly correlated with account_amount_added_12_24m and 5 other fieldsHigh correlation
sum_capital_paid_account_0_12m is highly correlated with account_amount_added_12_24m and 2 other fieldsHigh correlation
sum_capital_paid_account_12_24m is highly correlated with account_amount_added_12_24m and 3 other fieldsHigh correlation
sum_paid_inv_0_12m is highly correlated with max_paid_inv_0_12m and 3 other fieldsHigh correlation
account_amount_added_12_24m is highly correlated with sum_capital_paid_account_0_12m and 1 other fieldsHigh correlation
avg_payment_span_0_12m is highly correlated with avg_payment_span_0_3mHigh correlation
avg_payment_span_0_3m is highly correlated with avg_payment_span_0_12mHigh correlation
max_paid_inv_0_12m is highly correlated with max_paid_inv_0_24m and 1 other fieldsHigh correlation
max_paid_inv_0_24m is highly correlated with max_paid_inv_0_12mHigh correlation
num_active_inv is highly correlated with num_arch_ok_0_12m and 2 other fieldsHigh correlation
num_arch_ok_0_12m is highly correlated with num_active_inv and 2 other fieldsHigh correlation
num_arch_ok_12_24m is highly correlated with num_active_inv and 2 other fieldsHigh correlation
sum_capital_paid_account_0_12m is highly correlated with account_amount_added_12_24m and 1 other fieldsHigh correlation
sum_capital_paid_account_12_24m is highly correlated with account_amount_added_12_24m and 1 other fieldsHigh correlation
sum_paid_inv_0_12m is highly correlated with max_paid_inv_0_12m and 3 other fieldsHigh correlation
account_amount_added_12_24m is highly correlated with sum_capital_paid_account_0_12m and 1 other fieldsHigh correlation
avg_payment_span_0_12m is highly correlated with avg_payment_span_0_3mHigh correlation
avg_payment_span_0_3m is highly correlated with avg_payment_span_0_12mHigh correlation
max_paid_inv_0_12m is highly correlated with max_paid_inv_0_24m and 1 other fieldsHigh correlation
max_paid_inv_0_24m is highly correlated with max_paid_inv_0_12m and 1 other fieldsHigh correlation
num_active_div_by_paid_inv_0_12m is highly correlated with num_active_inv and 1 other fieldsHigh correlation
num_active_inv is highly correlated with num_active_div_by_paid_inv_0_12m and 1 other fieldsHigh correlation
num_arch_ok_0_12m is highly correlated with num_arch_ok_12_24m and 1 other fieldsHigh correlation
num_arch_ok_12_24m is highly correlated with num_arch_ok_0_12m and 1 other fieldsHigh correlation
num_unpaid_bills is highly correlated with num_active_div_by_paid_inv_0_12m and 2 other fieldsHigh correlation
sum_capital_paid_account_0_12m is highly correlated with account_amount_added_12_24m and 2 other fieldsHigh correlation
sum_capital_paid_account_12_24m is highly correlated with account_amount_added_12_24m and 1 other fieldsHigh correlation
sum_paid_inv_0_12m is highly correlated with max_paid_inv_0_12m and 3 other fieldsHigh correlation
account_amount_added_12_24m is highly correlated with sum_capital_paid_account_0_12m and 1 other fieldsHigh correlation
account_days_in_dc_12_24m is highly correlated with account_days_in_term_12_24mHigh correlation
account_days_in_term_12_24m is highly correlated with account_days_in_dc_12_24mHigh correlation
avg_payment_span_0_12m is highly correlated with avg_payment_span_0_3mHigh correlation
avg_payment_span_0_3m is highly correlated with avg_payment_span_0_12mHigh correlation
max_paid_inv_0_12m is highly correlated with max_paid_inv_0_24mHigh correlation
max_paid_inv_0_24m is highly correlated with max_paid_inv_0_12mHigh correlation
num_active_inv is highly correlated with num_arch_ok_0_12m and 3 other fieldsHigh correlation
num_arch_dc_0_12m is highly correlated with num_arch_dc_12_24mHigh correlation
num_arch_dc_12_24m is highly correlated with num_arch_dc_0_12mHigh correlation
num_arch_ok_0_12m is highly correlated with num_active_inv and 3 other fieldsHigh correlation
num_arch_ok_12_24m is highly correlated with num_active_inv and 2 other fieldsHigh correlation
num_arch_rem_0_12m is highly correlated with num_arch_ok_0_12m and 1 other fieldsHigh correlation
num_arch_written_off_12_24m is highly correlated with recovery_debtHigh correlation
num_unpaid_bills is highly correlated with num_active_inv and 1 other fieldsHigh correlation
recovery_debt is highly correlated with num_arch_written_off_12_24mHigh correlation
sum_capital_paid_account_0_12m is highly correlated with account_amount_added_12_24m and 1 other fieldsHigh correlation
sum_capital_paid_account_12_24m is highly correlated with account_amount_added_12_24m and 1 other fieldsHigh correlation
sum_paid_inv_0_12m is highly correlated with num_active_inv and 4 other fieldsHigh correlation
account_days_in_dc_12_24m has 11836 (11.8%) missing values Missing
account_days_in_rem_12_24m has 11836 (11.8%) missing values Missing
account_days_in_term_12_24m has 11836 (11.8%) missing values Missing
account_incoming_debt_vs_paid_0_24m has 59315 (59.3%) missing values Missing
avg_payment_span_0_12m has 23836 (23.8%) missing values Missing
avg_payment_span_0_3m has 49305 (49.3%) missing values Missing
num_active_div_by_paid_inv_0_12m has 22939 (22.9%) missing values Missing
num_arch_written_off_0_12m has 18078 (18.1%) missing values Missing
num_arch_written_off_12_24m has 18078 (18.1%) missing values Missing
account_days_in_dc_12_24m is highly skewed (γ1 = 38.39324078) Skewed
account_incoming_debt_vs_paid_0_24m is highly skewed (γ1 = 100.6863358) Skewed
recovery_debt is highly skewed (γ1 = 133.689137) Skewed
account_amount_added_12_24m has 71362 (71.4%) zeros Zeros
account_days_in_dc_12_24m has 87879 (87.9%) zeros Zeros
account_days_in_rem_12_24m has 78522 (78.5%) zeros Zeros
account_days_in_term_12_24m has 86932 (87.0%) zeros Zeros
account_incoming_debt_vs_paid_0_24m has 13072 (13.1%) zeros Zeros
max_paid_inv_0_12m has 21692 (21.7%) zeros Zeros
max_paid_inv_0_24m has 17615 (17.6%) zeros Zeros
num_active_div_by_paid_inv_0_12m has 48706 (48.7%) zeros Zeros
num_active_inv has 69515 (69.5%) zeros Zeros
num_arch_dc_0_12m has 95724 (95.7%) zeros Zeros
num_arch_dc_12_24m has 95991 (96.0%) zeros Zeros
num_arch_ok_0_12m has 27406 (27.4%) zeros Zeros
num_arch_ok_12_24m has 37905 (37.9%) zeros Zeros
num_arch_rem_0_12m has 76709 (76.7%) zeros Zeros
num_unpaid_bills has 52000 (52.0%) zeros Zeros
recovery_debt has 99754 (99.8%) zeros Zeros
sum_capital_paid_account_0_12m has 66011 (66.0%) zeros Zeros
sum_capital_paid_account_12_24m has 74788 (74.8%) zeros Zeros
sum_paid_inv_0_12m has 21692 (21.7%) zeros Zeros

Reproduction

Analysis started2022-09-22 14:43:48.293638
Analysis finished2022-09-22 14:44:45.314897
Duration57.02 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

account_amount_added_12_24m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct23721
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12255.14952
Minimum0
Maximum1128775
Zeros71362
Zeros (%)71.4%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2022-09-22T16:44:45.376666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34937.25
95-th percentile72967
Maximum1128775
Range1128775
Interquartile range (IQR)4937.25

Descriptive statistics

Standard deviation35481.48374
Coefficient of variation (CV)2.895230588
Kurtosis91.75787351
Mean12255.14952
Median Absolute Deviation (MAD)0
Skewness6.767239749
Sum1225220828
Variance1258935688
MonotonicityNot monotonic
2022-09-22T16:44:45.467465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
071362
71.4%
5034
 
< 0.1%
3034
 
< 0.1%
9025
 
< 0.1%
6022
 
< 0.1%
10019
 
< 0.1%
2012
 
< 0.1%
8010
 
< 0.1%
408
 
< 0.1%
74316
 
< 0.1%
Other values (23711)28444
 
28.5%
ValueCountFrequency (%)
071362
71.4%
11
 
< 0.1%
116
 
< 0.1%
132
 
< 0.1%
144
 
< 0.1%
151
 
< 0.1%
161
 
< 0.1%
176
 
< 0.1%
184
 
< 0.1%
2012
 
< 0.1%
ValueCountFrequency (%)
11287751
< 0.1%
11286541
< 0.1%
9635981
< 0.1%
9635941
< 0.1%
9634771
< 0.1%
9139431
< 0.1%
9138051
< 0.1%
8171341
< 0.1%
8170291
< 0.1%
7519711
< 0.1%

account_days_in_dc_12_24m
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct127
Distinct (%)0.1%
Missing11836
Missing (%)11.8%
Infinite0
Infinite (%)0.0%
Mean0.2230428863
Minimum0
Maximum365
Zeros87879
Zeros (%)87.9%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2022-09-22T16:44:45.558662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum365
Range365
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.808116523
Coefficient of variation (CV)26.04035761
Kurtosis1776.48798
Mean0.2230428863
Median Absolute Deviation (MAD)0
Skewness38.39324078
Sum19659
Variance33.73421754
MonotonicityNot monotonic
2022-09-22T16:44:45.641506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
087879
87.9%
911
 
< 0.1%
2810
 
< 0.1%
429
 
< 0.1%
679
 
< 0.1%
78
 
< 0.1%
567
 
< 0.1%
357
 
< 0.1%
997
 
< 0.1%
436
 
< 0.1%
Other values (117)187
 
0.2%
(Missing)11836
 
11.8%
ValueCountFrequency (%)
087879
87.9%
11
 
< 0.1%
36
 
< 0.1%
42
 
< 0.1%
51
 
< 0.1%
78
 
< 0.1%
911
 
< 0.1%
103
 
< 0.1%
113
 
< 0.1%
121
 
< 0.1%
ValueCountFrequency (%)
3651
< 0.1%
3621
< 0.1%
3502
< 0.1%
3221
< 0.1%
3181
< 0.1%
3161
< 0.1%
2911
< 0.1%
2892
< 0.1%
2761
< 0.1%
2711
< 0.1%

account_days_in_rem_12_24m
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct282
Distinct (%)0.3%
Missing11836
Missing (%)11.8%
Infinite0
Infinite (%)0.0%
Mean5.044622192
Minimum0
Maximum365
Zeros78522
Zeros (%)78.5%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2022-09-22T16:44:45.732862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile31
Maximum365
Range365
Interquartile range (IQR)0

Descriptive statistics

Standard deviation22.86397119
Coefficient of variation (CV)4.532345598
Kurtosis76.90992083
Mean5.044622192
Median Absolute Deviation (MAD)0
Skewness7.545146569
Sum444633
Variance522.7611784
MonotonicityNot monotonic
2022-09-22T16:44:45.820947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
078522
78.5%
1529
 
0.5%
2315
 
0.3%
21258
 
0.3%
15236
 
0.2%
16221
 
0.2%
3214
 
0.2%
14212
 
0.2%
22190
 
0.2%
13184
 
0.2%
Other values (272)7259
 
7.3%
(Missing)11836
 
11.8%
ValueCountFrequency (%)
078522
78.5%
1529
 
0.5%
2315
 
0.3%
3214
 
0.2%
4172
 
0.2%
587
 
0.1%
6129
 
0.1%
7178
 
0.2%
8153
 
0.2%
9137
 
0.1%
ValueCountFrequency (%)
36550
0.1%
3621
 
< 0.1%
3581
 
< 0.1%
3561
 
< 0.1%
3542
 
< 0.1%
3531
 
< 0.1%
3511
 
< 0.1%
3461
 
< 0.1%
3431
 
< 0.1%
3411
 
< 0.1%

account_days_in_term_12_24m
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct64
Distinct (%)0.1%
Missing11836
Missing (%)11.8%
Infinite0
Infinite (%)0.0%
Mean0.2868958475
Minimum0
Maximum97
Zeros86932
Zeros (%)87.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2022-09-22T16:44:45.915997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum97
Range97
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.929910479
Coefficient of variation (CV)10.21245342
Kurtosis188.5714584
Mean0.2868958475
Median Absolute Deviation (MAD)0
Skewness12.49859581
Sum25287
Variance8.584375415
MonotonicityNot monotonic
2022-09-22T16:44:46.001077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
086932
87.0%
34274
 
0.3%
756
 
0.1%
152
 
0.1%
249
 
< 0.1%
1142
 
< 0.1%
2239
 
< 0.1%
838
 
< 0.1%
1538
 
< 0.1%
2336
 
< 0.1%
Other values (54)584
 
0.6%
(Missing)11836
 
11.8%
ValueCountFrequency (%)
086932
87.0%
152
 
0.1%
249
 
< 0.1%
330
 
< 0.1%
420
 
< 0.1%
521
 
< 0.1%
624
 
< 0.1%
756
 
0.1%
838
 
< 0.1%
931
 
< 0.1%
ValueCountFrequency (%)
972
 
< 0.1%
911
 
< 0.1%
801
 
< 0.1%
681
 
< 0.1%
672
 
< 0.1%
653
< 0.1%
641
 
< 0.1%
635
< 0.1%
611
 
< 0.1%
602
 
< 0.1%

account_incoming_debt_vs_paid_0_24m
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct23674
Distinct (%)58.2%
Missing59315
Missing (%)59.3%
Infinite0
Infinite (%)0.0%
Mean1.331291764
Minimum0
Maximum3914
Zeros13072
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2022-09-22T16:44:46.087626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.1520819113
Q30.662952183
95-th percentile2.792796942
Maximum3914
Range3914
Interquartile range (IQR)0.662952183

Descriptive statistics

Standard deviation26.48229928
Coefficient of variation (CV)19.8921829
Kurtosis12826.8267
Mean1.331291764
Median Absolute Deviation (MAD)0.1520819113
Skewness100.6863358
Sum54131.65444
Variance701.312175
MonotonicityNot monotonic
2022-09-22T16:44:46.177561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013072
 
13.1%
8.0344262357
 
0.1%
0.00430073271718
 
< 0.1%
2.151462995 × 10-517
 
< 0.1%
0.00365019011415
 
< 0.1%
1.101746268 × 10-514
 
< 0.1%
0.000113604089712
 
< 0.1%
0.0111248799112
 
< 0.1%
61.1760563411
 
< 0.1%
0.00096805421111
 
< 0.1%
Other values (23664)27422
27.4%
(Missing)59315
59.3%
ValueCountFrequency (%)
013072
13.1%
3.788294926 × 10-63
 
< 0.1%
4.933934615 × 10-62
 
< 0.1%
5.956245421 × 10-61
 
< 0.1%
6.034529578 × 10-61
 
< 0.1%
6.170173382 × 10-61
 
< 0.1%
6.584318786 × 10-61
 
< 0.1%
6.708482877 × 10-61
 
< 0.1%
7.028048943 × 10-62
 
< 0.1%
7.125907662 × 10-61
 
< 0.1%
ValueCountFrequency (%)
39141
 
< 0.1%
1443.481
 
< 0.1%
1435.583
< 0.1%
1336.9354841
 
< 0.1%
1176.8888891
 
< 0.1%
329.48148151
 
< 0.1%
299.73214291
 
< 0.1%
2941
 
< 0.1%
270.1951221
 
< 0.1%
245.60869571
 
< 0.1%

age
Real number (ℝ≥0)

Distinct79
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.01628391
Minimum18
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2022-09-22T16:44:46.271762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile19
Q125
median34
Q345
95-th percentile60
Maximum100
Range82
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.00130583
Coefficient of variation (CV)0.3609841001
Kurtosis-0.04225289525
Mean36.01628391
Median Absolute Deviation (MAD)10
Skewness0.6895777822
Sum3600764
Variance169.0339534
MonotonicityNot monotonic
2022-09-22T16:44:46.358176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
183704
 
3.7%
223477
 
3.5%
213470
 
3.5%
203258
 
3.3%
233256
 
3.3%
243013
 
3.0%
283004
 
3.0%
262979
 
3.0%
292967
 
3.0%
252949
 
2.9%
Other values (69)67899
67.9%
ValueCountFrequency (%)
183704
3.7%
192653
2.7%
203258
3.3%
213470
3.5%
223477
3.5%
233256
3.3%
243013
3.0%
252949
2.9%
262979
3.0%
272920
2.9%
ValueCountFrequency (%)
1001
 
< 0.1%
951
 
< 0.1%
941
 
< 0.1%
932
 
< 0.1%
921
 
< 0.1%
912
 
< 0.1%
902
 
< 0.1%
894
< 0.1%
886
< 0.1%
875
< 0.1%

avg_payment_span_0_12m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7939
Distinct (%)10.4%
Missing23836
Missing (%)23.8%
Infinite0
Infinite (%)0.0%
Mean17.97147269
Minimum0
Maximum260
Zeros500
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2022-09-22T16:44:46.450579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.135063559
Q110.8
median14.90909091
Q321
95-th percentile41
Maximum260
Range260
Interquartile range (IQR)10.2

Descriptive statistics

Standard deviation12.75106572
Coefficient of variation (CV)0.7095170184
Kurtosis20.71225599
Mean17.97147269
Median Absolute Deviation (MAD)4.909090909
Skewness3.203545818
Sum1368347.931
Variance162.589677
MonotonicityNot monotonic
2022-09-22T16:44:46.680999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142144
 
2.1%
131833
 
1.8%
151281
 
1.3%
121227
 
1.2%
161166
 
1.2%
111081
 
1.1%
101037
 
1.0%
9996
 
1.0%
17962
 
1.0%
7902
 
0.9%
Other values (7929)63511
63.5%
(Missing)23836
 
23.8%
ValueCountFrequency (%)
0500
0.5%
0.16666666673
 
< 0.1%
0.21
 
< 0.1%
0.22222222221
 
< 0.1%
0.253
 
< 0.1%
0.28571428571
 
< 0.1%
0.333333333310
 
< 0.1%
0.3751
 
< 0.1%
0.537
 
< 0.1%
0.51612903232
 
< 0.1%
ValueCountFrequency (%)
2601
< 0.1%
2241
< 0.1%
2171
< 0.1%
2041
< 0.1%
1871
< 0.1%
1841
< 0.1%
1821
< 0.1%
1741
< 0.1%
1731
< 0.1%
1692
< 0.1%

avg_payment_span_0_3m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2256
Distinct (%)4.5%
Missing49305
Missing (%)49.3%
Infinite0
Infinite (%)0.0%
Mean14.98978561
Minimum0
Maximum87
Zeros802
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2022-09-22T16:44:46.766867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q18.4
median13
Q318.28571429
95-th percentile36
Maximum87
Range87
Interquartile range (IQR)9.885714286

Descriptive statistics

Standard deviation10.29742038
Coefficient of variation (CV)0.6869624853
Kurtosis4.824439581
Mean14.98978561
Median Absolute Deviation (MAD)5
Skewness1.783164222
Sum759547.4267
Variance106.0368664
MonotonicityNot monotonic
2022-09-22T16:44:46.856405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142764
 
2.8%
132269
 
2.3%
61348
 
1.3%
121335
 
1.3%
161331
 
1.3%
71327
 
1.3%
151295
 
1.3%
101252
 
1.3%
111211
 
1.2%
91150
 
1.2%
Other values (2246)35389
35.4%
(Missing)49305
49.3%
ValueCountFrequency (%)
0802
0.8%
0.083333333331
 
< 0.1%
0.16666666673
 
< 0.1%
0.21
 
< 0.1%
0.254
 
< 0.1%
0.28571428571
 
< 0.1%
0.333333333310
 
< 0.1%
0.36363636361
 
< 0.1%
0.38888888891
 
< 0.1%
0.41
 
< 0.1%
ValueCountFrequency (%)
871
 
< 0.1%
861
 
< 0.1%
845
< 0.1%
835
< 0.1%
821
 
< 0.1%
812
 
< 0.1%
804
< 0.1%
79.666666671
 
< 0.1%
791
 
< 0.1%
782
 
< 0.1%

max_paid_inv_0_12m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct12497
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9203.654217
Minimum0
Maximum279000
Zeros21692
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2022-09-22T16:44:46.941601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12000
median6052
Q311380
95-th percentile29272.5
Maximum279000
Range279000
Interquartile range (IQR)9380

Descriptive statistics

Standard deviation13512.16723
Coefficient of variation (CV)1.468130691
Kurtosis56.10386348
Mean9203.654217
Median Absolute Deviation (MAD)4772
Skewness5.653271466
Sum920144534
Variance182578663.2
MonotonicityNot monotonic
2022-09-22T16:44:47.021347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
021692
 
21.7%
5290440
 
0.4%
895397
 
0.4%
4290364
 
0.4%
5000290
 
0.3%
6790278
 
0.3%
4790274
 
0.3%
3290261
 
0.3%
2290251
 
0.3%
6290237
 
0.2%
Other values (12487)75492
75.5%
ValueCountFrequency (%)
021692
21.7%
901
 
< 0.1%
1752
 
< 0.1%
2101
 
< 0.1%
2702
 
< 0.1%
2902
 
< 0.1%
2954
 
< 0.1%
3003
 
< 0.1%
3201
 
< 0.1%
3401
 
< 0.1%
ValueCountFrequency (%)
2790001
 
< 0.1%
2702951
 
< 0.1%
2643003
< 0.1%
2603951
 
< 0.1%
2518901
 
< 0.1%
2451102
 
< 0.1%
2400001
 
< 0.1%
2357902
 
< 0.1%
2338907
< 0.1%
2305451
 
< 0.1%

max_paid_inv_0_24m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct12932
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11215.12082
Minimum0
Maximum538500
Zeros17615
Zeros (%)17.6%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2022-09-22T16:44:47.106341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13350
median7580
Q313783
95-th percentile34295
Maximum538500
Range538500
Interquartile range (IQR)10433

Descriptive statistics

Standard deviation15256.41494
Coefficient of variation (CV)1.360343342
Kurtosis56.77507679
Mean11215.12082
Median Absolute Deviation (MAD)5005
Skewness5.367850225
Sum1121242919
Variance232758196.7
MonotonicityNot monotonic
2022-09-22T16:44:47.198327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017615
 
17.6%
5290433
 
0.4%
4290280
 
0.3%
5000249
 
0.2%
6290249
 
0.2%
895247
 
0.2%
9290245
 
0.2%
6790240
 
0.2%
3290235
 
0.2%
4790225
 
0.2%
Other values (12922)79958
80.0%
ValueCountFrequency (%)
017615
17.6%
901
 
< 0.1%
1751
 
< 0.1%
2102
 
< 0.1%
2701
 
< 0.1%
2901
 
< 0.1%
2953
 
< 0.1%
3003
 
< 0.1%
3201
 
< 0.1%
3701
 
< 0.1%
ValueCountFrequency (%)
5385001
 
< 0.1%
2790001
 
< 0.1%
2702951
 
< 0.1%
2643003
< 0.1%
2603951
 
< 0.1%
2518901
 
< 0.1%
2451102
< 0.1%
2400001
 
< 0.1%
2357902
< 0.1%
2349951
 
< 0.1%

num_active_div_by_paid_inv_0_12m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct861
Distinct (%)1.1%
Missing22939
Missing (%)22.9%
Infinite0
Infinite (%)0.0%
Mean0.114840286
Minimum0
Maximum9
Zeros48706
Zeros (%)48.7%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2022-09-22T16:44:47.288065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.1
95-th percentile0.5
Maximum9
Range9
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.293483024
Coefficient of variation (CV)2.555575525
Kurtosis82.01839065
Mean0.114840286
Median Absolute Deviation (MAD)0
Skewness6.366929886
Sum8846.951109
Variance0.08613228539
MonotonicityNot monotonic
2022-09-22T16:44:47.373016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
048706
48.7%
12425
 
2.4%
0.52273
 
2.3%
0.33333333331918
 
1.9%
0.251683
 
1.7%
0.21490
 
1.5%
0.16666666671270
 
1.3%
0.14285714291098
 
1.1%
0.125905
 
0.9%
0.1111111111826
 
0.8%
Other values (851)14443
 
14.4%
(Missing)22939
22.9%
ValueCountFrequency (%)
048706
48.7%
0.0066666666671
 
< 0.1%
0.0071428571431
 
< 0.1%
0.0072992700733
 
< 0.1%
0.0074626865671
 
< 0.1%
0.0075187969921
 
< 0.1%
0.0079365079374
 
< 0.1%
0.008264462811
 
< 0.1%
0.0083333333331
 
< 0.1%
0.0084745762712
 
< 0.1%
ValueCountFrequency (%)
92
 
< 0.1%
81
 
< 0.1%
72
 
< 0.1%
65
 
< 0.1%
510
 
< 0.1%
4.51
 
< 0.1%
414
 
< 0.1%
3.52
 
< 0.1%
375
0.1%
2.58
 
< 0.1%

num_active_inv
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5994038569
Minimum0
Maximum47
Zeros69515
Zeros (%)69.5%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2022-09-22T16:44:47.453093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum47
Range47
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.550026416
Coefficient of variation (CV)2.585946684
Kurtosis108.5395278
Mean0.5994038569
Median Absolute Deviation (MAD)0
Skewness7.918090283
Sum59926
Variance2.40258189
MonotonicityNot monotonic
2022-09-22T16:44:47.526486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
069515
69.5%
118493
 
18.5%
26250
 
6.3%
32529
 
2.5%
41207
 
1.2%
5649
 
0.6%
6398
 
0.4%
7225
 
0.2%
8154
 
0.2%
9111
 
0.1%
Other values (27)445
 
0.4%
ValueCountFrequency (%)
069515
69.5%
118493
 
18.5%
26250
 
6.3%
32529
 
2.5%
41207
 
1.2%
5649
 
0.6%
6398
 
0.4%
7225
 
0.2%
8154
 
0.2%
9111
 
0.1%
ValueCountFrequency (%)
471
 
< 0.1%
382
 
< 0.1%
373
< 0.1%
352
 
< 0.1%
334
< 0.1%
311
 
< 0.1%
302
 
< 0.1%
293
< 0.1%
287
< 0.1%
277
< 0.1%

num_arch_dc_0_12m
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06174481876
Minimum0
Maximum17
Zeros95724
Zeros (%)95.7%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2022-09-22T16:44:47.592758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum17
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3746913273
Coefficient of variation (CV)6.068384924
Kurtosis265.3512069
Mean0.06174481876
Median Absolute Deviation (MAD)0
Skewness12.27561745
Sum6173
Variance0.1403935907
MonotonicityNot monotonic
2022-09-22T16:44:47.652748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
095724
95.7%
13192
 
3.2%
2681
 
0.7%
3196
 
0.2%
477
 
0.1%
635
 
< 0.1%
530
 
< 0.1%
721
 
< 0.1%
85
 
< 0.1%
134
 
< 0.1%
Other values (5)11
 
< 0.1%
ValueCountFrequency (%)
095724
95.7%
13192
 
3.2%
2681
 
0.7%
3196
 
0.2%
477
 
0.1%
530
 
< 0.1%
635
 
< 0.1%
721
 
< 0.1%
85
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
171
 
< 0.1%
161
 
< 0.1%
134
 
< 0.1%
113
 
< 0.1%
104
 
< 0.1%
92
 
< 0.1%
85
 
< 0.1%
721
< 0.1%
635
< 0.1%
530
< 0.1%

num_arch_dc_12_24m
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05936424742
Minimum0
Maximum13
Zeros95991
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2022-09-22T16:44:47.718173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3662243329
Coefficient of variation (CV)6.169105965
Kurtosis184.2389426
Mean0.05936424742
Median Absolute Deviation (MAD)0
Skewness10.91096016
Sum5935
Variance0.134120262
MonotonicityNot monotonic
2022-09-22T16:44:47.782259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
095991
96.0%
12913
 
2.9%
2665
 
0.7%
3195
 
0.2%
4106
 
0.1%
546
 
< 0.1%
724
 
< 0.1%
617
 
< 0.1%
106
 
< 0.1%
86
 
< 0.1%
Other values (3)7
 
< 0.1%
ValueCountFrequency (%)
095991
96.0%
12913
 
2.9%
2665
 
0.7%
3195
 
0.2%
4106
 
0.1%
546
 
< 0.1%
617
 
< 0.1%
724
 
< 0.1%
86
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
131
 
< 0.1%
114
 
< 0.1%
106
 
< 0.1%
92
 
< 0.1%
86
 
< 0.1%
724
 
< 0.1%
617
 
< 0.1%
546
 
< 0.1%
4106
0.1%
3195
0.2%

num_arch_ok_0_12m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct201
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.275826198
Minimum0
Maximum261
Zeros27406
Zeros (%)27.4%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2022-09-22T16:44:47.873572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile30
Maximum261
Range261
Interquartile range (IQR)7

Descriptive statistics

Standard deviation16.03036935
Coefficient of variation (CV)2.203236981
Kurtosis46.01920749
Mean7.275826198
Median Absolute Deviation (MAD)2
Skewness5.722424978
Sum727408
Variance256.9727414
MonotonicityNot monotonic
2022-09-22T16:44:47.966656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
027406
27.4%
114098
14.1%
29929
 
9.9%
37370
 
7.4%
45808
 
5.8%
54717
 
4.7%
63822
 
3.8%
73130
 
3.1%
82497
 
2.5%
92126
 
2.1%
Other values (191)19073
19.1%
ValueCountFrequency (%)
027406
27.4%
114098
14.1%
29929
 
9.9%
37370
 
7.4%
45808
 
5.8%
54717
 
4.7%
63822
 
3.8%
73130
 
3.1%
82497
 
2.5%
92126
 
2.1%
ValueCountFrequency (%)
2611
 
< 0.1%
2481
 
< 0.1%
2471
 
< 0.1%
2361
 
< 0.1%
2321
 
< 0.1%
2311
 
< 0.1%
2255
< 0.1%
2243
< 0.1%
2235
< 0.1%
2227
< 0.1%

num_arch_ok_12_24m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct200
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.369798752
Minimum0
Maximum313
Zeros37905
Zeros (%)37.9%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2022-09-22T16:44:48.055038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile28
Maximum313
Range313
Interquartile range (IQR)6

Descriptive statistics

Standard deviation15.35024427
Coefficient of variation (CV)2.409847606
Kurtosis82.59847026
Mean6.369798752
Median Absolute Deviation (MAD)2
Skewness7.153601334
Sum636827
Variance235.6299992
MonotonicityNot monotonic
2022-09-22T16:44:48.139719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
037905
37.9%
111038
 
11.0%
28305
 
8.3%
36255
 
6.3%
44824
 
4.8%
54138
 
4.1%
63415
 
3.4%
72761
 
2.8%
82369
 
2.4%
91906
 
1.9%
Other values (190)17060
17.1%
ValueCountFrequency (%)
037905
37.9%
111038
 
11.0%
28305
 
8.3%
36255
 
6.3%
44824
 
4.8%
54138
 
4.1%
63415
 
3.4%
72761
 
2.8%
82369
 
2.4%
91906
 
1.9%
ValueCountFrequency (%)
3131
 
< 0.1%
3041
 
< 0.1%
3022
 
< 0.1%
3011
 
< 0.1%
2936
< 0.1%
2922
 
< 0.1%
2902
 
< 0.1%
2881
 
< 0.1%
2781
 
< 0.1%
2772
 
< 0.1%

num_arch_rem_0_12m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4694426662
Minimum0
Maximum42
Zeros76709
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2022-09-22T16:44:48.223240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.360348893
Coefficient of variation (CV)2.897795601
Kurtosis129.526541
Mean0.4694426662
Median Absolute Deviation (MAD)0
Skewness8.358018709
Sum46933
Variance1.850549111
MonotonicityNot monotonic
2022-09-22T16:44:48.292373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
076709
76.7%
113521
 
13.5%
24758
 
4.8%
32270
 
2.3%
41092
 
1.1%
5602
 
0.6%
6322
 
0.3%
7222
 
0.2%
8115
 
0.1%
980
 
0.1%
Other values (21)285
 
0.3%
ValueCountFrequency (%)
076709
76.7%
113521
 
13.5%
24758
 
4.8%
32270
 
2.3%
41092
 
1.1%
5602
 
0.6%
6322
 
0.3%
7222
 
0.2%
8115
 
0.1%
980
 
0.1%
ValueCountFrequency (%)
423
 
< 0.1%
392
 
< 0.1%
2910
< 0.1%
277
< 0.1%
265
 
< 0.1%
257
< 0.1%
2416
< 0.1%
236
 
< 0.1%
221
 
< 0.1%
218
< 0.1%

num_arch_written_off_0_12m
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing18078
Missing (%)18.1%
Memory size781.2 KiB
0.0
81888 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.081888
81.9%
1.010
 
< 0.1%
(Missing)18078
 
18.1%

Length

2022-09-22T16:44:48.362225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-09-22T16:44:48.406508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.081888
> 99.9%
1.010
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

num_arch_written_off_12_24m
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing18078
Missing (%)18.1%
Memory size781.2 KiB
0.0
81887 
1.0
 
9
2.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.081887
81.9%
1.09
 
< 0.1%
2.02
 
< 0.1%
(Missing)18078
 
18.1%

Length

2022-09-22T16:44:48.449692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-09-22T16:44:48.493541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.081887
> 99.9%
1.09
 
< 0.1%
2.02
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

num_unpaid_bills
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct132
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.141563975
Minimum0
Maximum182
Zeros52000
Zeros (%)52.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2022-09-22T16:44:48.548882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile10
Maximum182
Range182
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.300977704
Coefficient of variation (CV)2.942231835
Kurtosis173.107918
Mean2.141563975
Median Absolute Deviation (MAD)0
Skewness10.40120692
Sum214105
Variance39.70232003
MonotonicityNot monotonic
2022-09-22T16:44:48.636785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
052000
52.0%
119300
 
19.3%
29216
 
9.2%
35007
 
5.0%
43034
 
3.0%
52016
 
2.0%
61519
 
1.5%
71157
 
1.2%
8891
 
0.9%
9756
 
0.8%
Other values (122)5080
 
5.1%
ValueCountFrequency (%)
052000
52.0%
119300
 
19.3%
29216
 
9.2%
35007
 
5.0%
43034
 
3.0%
52016
 
2.0%
61519
 
1.5%
71157
 
1.2%
8891
 
0.9%
9756
 
0.8%
ValueCountFrequency (%)
1821
 
< 0.1%
1621
 
< 0.1%
1602
< 0.1%
1591
 
< 0.1%
1581
 
< 0.1%
1533
< 0.1%
1521
 
< 0.1%
1501
 
< 0.1%
1491
 
< 0.1%
1472
< 0.1%

recovery_debt
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct111
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.035428503
Minimum0
Maximum36479
Zeros99754
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2022-09-22T16:44:48.867978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum36479
Range36479
Interquartile range (IQR)0

Descriptive statistics

Standard deviation163.934564
Coefficient of variation (CV)40.62383062
Kurtosis26142.93973
Mean4.035428503
Median Absolute Deviation (MAD)0
Skewness133.689137
Sum403446
Variance26874.54126
MonotonicityNot monotonic
2022-09-22T16:44:48.952157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
099754
99.8%
50047
 
< 0.1%
100024
 
< 0.1%
15007
 
< 0.1%
21906
 
< 0.1%
6014
 
< 0.1%
12754
 
< 0.1%
19393
 
< 0.1%
20803
 
< 0.1%
25803
 
< 0.1%
Other values (101)121
 
0.1%
ValueCountFrequency (%)
099754
99.8%
471
 
< 0.1%
901
 
< 0.1%
991
 
< 0.1%
3482
 
< 0.1%
4001
 
< 0.1%
50047
 
< 0.1%
5191
 
< 0.1%
5211
 
< 0.1%
5301
 
< 0.1%
ValueCountFrequency (%)
364791
< 0.1%
164111
< 0.1%
111901
< 0.1%
102301
< 0.1%
79101
< 0.1%
72002
< 0.1%
66301
< 0.1%
62851
< 0.1%
60651
< 0.1%
55901
< 0.1%

sum_capital_paid_account_0_12m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22580
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10816.06539
Minimum0
Maximum571475
Zeros66011
Zeros (%)66.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2022-09-22T16:44:49.067598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39029.75
95-th percentile57993.75
Maximum571475
Range571475
Interquartile range (IQR)9029.75

Descriptive statistics

Standard deviation26463.97217
Coefficient of variation (CV)2.446728198
Kurtosis40.04623822
Mean10816.06539
Median Absolute Deviation (MAD)0
Skewness4.920542743
Sum1081346953
Variance700341823
MonotonicityNot monotonic
2022-09-22T16:44:49.176352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
066011
66.0%
30040
 
< 0.1%
299037
 
< 0.1%
70027
 
< 0.1%
3106726
 
< 0.1%
338523
 
< 0.1%
529021
 
< 0.1%
264120
 
< 0.1%
3018
 
< 0.1%
10018
 
< 0.1%
Other values (22570)33735
33.7%
ValueCountFrequency (%)
066011
66.0%
19
 
< 0.1%
22
 
< 0.1%
317
 
< 0.1%
414
 
< 0.1%
56
 
< 0.1%
61
 
< 0.1%
73
 
< 0.1%
91
 
< 0.1%
103
 
< 0.1%
ValueCountFrequency (%)
5714751
 
< 0.1%
5093481
 
< 0.1%
4906724
< 0.1%
4527151
 
< 0.1%
4513511
 
< 0.1%
4476781
 
< 0.1%
4185191
 
< 0.1%
4132204
< 0.1%
3920761
 
< 0.1%
3918361
 
< 0.1%

sum_capital_paid_account_12_24m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct16667
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6542.895325
Minimum0
Maximum341859
Zeros74788
Zeros (%)74.8%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2022-09-22T16:44:49.283236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q385
95-th percentile39200.75
Maximum341859
Range341859
Interquartile range (IQR)85

Descriptive statistics

Standard deviation19041.22359
Coefficient of variation (CV)2.910213696
Kurtosis43.76498797
Mean6542.895325
Median Absolute Deviation (MAD)0
Skewness5.410064826
Sum654132503
Variance362568195.6
MonotonicityNot monotonic
2022-09-22T16:44:49.382177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
074788
74.8%
30072
 
0.1%
9697437
 
< 0.1%
2039030
 
< 0.1%
348527
 
< 0.1%
219025
 
< 0.1%
599022
 
< 0.1%
5021
 
< 0.1%
89520
 
< 0.1%
319019
 
< 0.1%
Other values (16657)24915
 
24.9%
ValueCountFrequency (%)
074788
74.8%
16
 
< 0.1%
25
 
< 0.1%
36
 
< 0.1%
43
 
< 0.1%
52
 
< 0.1%
64
 
< 0.1%
72
 
< 0.1%
91
 
< 0.1%
104
 
< 0.1%
ValueCountFrequency (%)
3418591
< 0.1%
3365682
< 0.1%
3339001
< 0.1%
3211021
< 0.1%
3158381
< 0.1%
3147381
< 0.1%
3145031
< 0.1%
3135022
< 0.1%
3106821
< 0.1%
3079751
< 0.1%

sum_paid_inv_0_12m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct38387
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39208.80222
Minimum0
Maximum2962870
Zeros21692
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2022-09-22T16:44:49.478638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12600
median15995
Q343844.25
95-th percentile150288.5
Maximum2962870
Range2962870
Interquartile range (IQR)41244.25

Descriptive statistics

Standard deviation90649.28528
Coefficient of variation (CV)2.311962624
Kurtosis411.8589793
Mean39208.80222
Median Absolute Deviation (MAD)15995
Skewness15.52557252
Sum3919939211
Variance8217292921
MonotonicityNot monotonic
2022-09-22T16:44:49.570226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
021692
 
21.7%
895269
 
0.3%
1790153
 
0.2%
200099
 
0.1%
100094
 
0.1%
229090
 
0.1%
199081
 
0.1%
329077
 
0.1%
249075
 
0.1%
129075
 
0.1%
Other values (38377)77271
77.3%
ValueCountFrequency (%)
021692
21.7%
901
 
< 0.1%
1752
 
< 0.1%
2101
 
< 0.1%
2702
 
< 0.1%
2902
 
< 0.1%
2954
 
< 0.1%
3003
 
< 0.1%
3201
 
< 0.1%
3602
 
< 0.1%
ValueCountFrequency (%)
29628701
 
< 0.1%
28539921
 
< 0.1%
28356522
< 0.1%
27926941
 
< 0.1%
27892043
< 0.1%
27688353
< 0.1%
27468893
< 0.1%
27443622
< 0.1%
27254052
< 0.1%
27199171
 
< 0.1%

time_hours
Real number (ℝ≥0)

Distinct50650
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.32977989
Minimum0.0002777777778
Maximum23.99972222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2022-09-22T16:44:49.665308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0002777777778
5-th percentile7.364930556
Q111.62270833
median15.79277778
Q319.54201389
95-th percentile22.33972222
Maximum23.99972222
Range23.99944444
Interquartile range (IQR)7.919305556

Descriptive statistics

Standard deviation5.031360239
Coefficient of variation (CV)0.3282082505
Kurtosis-0.2260409869
Mean15.32977989
Median Absolute Deviation (MAD)3.934444444
Skewness-0.4981009139
Sum1532610.074
Variance25.31458585
MonotonicityNot monotonic
2022-09-22T16:44:49.755154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.3236111111
 
< 0.1%
19.69
 
< 0.1%
13.216666679
 
< 0.1%
15.472777789
 
< 0.1%
19.806944449
 
< 0.1%
17.088888898
 
< 0.1%
15.822222228
 
< 0.1%
18.176111118
 
< 0.1%
20.275277788
 
< 0.1%
18.094444448
 
< 0.1%
Other values (50640)99889
99.9%
ValueCountFrequency (%)
0.00027777777781
 
< 0.1%
0.0016666666672
< 0.1%
0.0033333333331
 
< 0.1%
0.0036111111111
 
< 0.1%
0.0044444444441
 
< 0.1%
0.0055555555561
 
< 0.1%
0.0063888888891
 
< 0.1%
0.0072222222223
< 0.1%
0.0077777777781
 
< 0.1%
0.0080555555561
 
< 0.1%
ValueCountFrequency (%)
23.999722222
< 0.1%
23.998611111
 
< 0.1%
23.998333331
 
< 0.1%
23.996666671
 
< 0.1%
23.996388894
< 0.1%
23.995833331
 
< 0.1%
23.994444442
< 0.1%
23.993055562
< 0.1%
23.992777781
 
< 0.1%
23.991944441
 
< 0.1%

Interactions

2022-09-22T16:44:41.657073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:43:56.260856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:43:58.215701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:00.388639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:02.389727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:04.466016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:06.761393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:08.660852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:10.742073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:12.601506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:14.744517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:16.698203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:18.694029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:20.574114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:22.773845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:24.838152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:26.920428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:29.043676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:30.965464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:33.073007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:35.101685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:37.246145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:39.665397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:41.745807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:43:56.346846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:43:58.302859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:00.473348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:02.472360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:04.611376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:06.847305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:08.742137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:10.866109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:12.699382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:14.829186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:16.777194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:18.779867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:20.670532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:22.857739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:24.926368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:27.005509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:29.126605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:31.051750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:33.164304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:35.188216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:37.333309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:39.751888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:41.830416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:43:56.434933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:43:58.397164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:00.574074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:02.557667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:04.786932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:06.933692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:08.826268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:10.952721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:12.800193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:14.914909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:16.860129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:18.864196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:20.777512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:22.943670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:25.017034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-09-22T16:44:00.125035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:02.130227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:04.174145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:06.338868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:08.409725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:10.460462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:12.359524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:14.476253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:16.442659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:18.443884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:20.325385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:22.510472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:24.442955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:26.656290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:28.781737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:30.700021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:32.810844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:34.838617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:36.983233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:39.171242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:41.376582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:43.598934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:43:58.045678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:00.217118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:02.219664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:04.261213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:06.427771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:08.496124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:10.550092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:12.440992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:14.566317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:16.532136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:18.529269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:20.411828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:22.600478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:24.665646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:26.744825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:28.871901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:30.794909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:32.902558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:34.928469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:37.073838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:39.486688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:41.466456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:43.684577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:43:58.133420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:00.305140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:02.306529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:04.344339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:06.514845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:08.582053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:10.637451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:12.520141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:14.656303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:16.618037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:18.611186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:20.495668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:22.690198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:24.756448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:26.835714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:28.959722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:30.885469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:32.991168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:35.017126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:37.162458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:39.578744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-22T16:44:41.559483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-09-22T16:44:49.852140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-22T16:44:50.056800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-22T16:44:50.257046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-22T16:44:50.434661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-22T16:44:50.524553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-22T16:44:43.818159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-22T16:44:44.402859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-22T16:44:44.997651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-22T16:44:45.167505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

account_amount_added_12_24maccount_days_in_dc_12_24maccount_days_in_rem_12_24maccount_days_in_term_12_24maccount_incoming_debt_vs_paid_0_24mageavg_payment_span_0_12mavg_payment_span_0_3mmax_paid_inv_0_12mmax_paid_inv_0_24mnum_active_div_by_paid_inv_0_12mnum_active_invnum_arch_dc_0_12mnum_arch_dc_12_24mnum_arch_ok_0_12mnum_arch_ok_12_24mnum_arch_rem_0_12mnum_arch_written_off_0_12mnum_arch_written_off_12_24mnum_unpaid_billsrecovery_debtsum_capital_paid_account_0_12msum_capital_paid_account_12_24msum_paid_inv_0_12mtime_hours
000.00.00.00.0000002012.6923088.33333331638.031638.00.153846200131400.00.020001788399.653333
100.00.00.0NaN5025.83333325.00000013749.013749.00.00000000091930.00.000004901413.181389
200.00.00.0NaN2220.00000018.00000029890.029890.00.07142910011030.00.0100012483911.561944
30NaNNaNNaNNaN364.6875004.88888940040.040040.00.031250100312100.00.0100032467615.751111
400.00.00.0NaN2513.00000013.0000007100.07100.00.0000000001000.00.00000710012.698611
500.00.00.0NaN18NaNNaN0.00.0NaN000000NaNNaN0000018.328333
600.0142.00.00.000000493.0000003.0000002373.02373.00.0000000001000.00.000187608337237310.244444
7572290.00.00.00.2322443426.93023325.8666678655.09645.00.083333200021525700.00.0370422063533645725712.192778
81489220.047.00.00.9690554033.72727337.5714296075.09090.00.8181829003230.00.0230104643323812439021.411111
900.00.00.0NaN4721.00000021.25000036985.036985.00.00000000051000.00.000007862013.340833

Last rows

account_amount_added_12_24maccount_days_in_dc_12_24maccount_days_in_rem_12_24maccount_days_in_term_12_24maccount_incoming_debt_vs_paid_0_24mageavg_payment_span_0_12mavg_payment_span_0_3mmax_paid_inv_0_12mmax_paid_inv_0_24mnum_active_div_by_paid_inv_0_12mnum_active_invnum_arch_dc_0_12mnum_arch_dc_12_24mnum_arch_ok_0_12mnum_arch_ok_12_24mnum_arch_rem_0_12mnum_arch_written_off_0_12mnum_arch_written_off_12_24mnum_unpaid_billsrecovery_debtsum_capital_paid_account_0_12msum_capital_paid_account_12_24msum_paid_inv_0_12mtime_hours
99966166420.059.00.00.0000004040.00000016.00000011835.011835.00.1428571002550.00.02012738108524424417.471389
99967323550.0125.044.00.6658292812.000000NaN9330.09330.00.0000000001000.00.016020633223551022513.057500
9996800.00.00.0NaN4517.09090920.71428612264.012264.00.037975300774400.00.0300027613519.786944
999690365.00.00.00.37160431NaNNaN895.0895.0NaN000000NaNNaN207695102589511.290833
99970884050.015.00.00.67200021NaNNaN0.06242.0NaN0000200.00.090288702577109.060833
9997100.00.00.0NaN3310.333333NaN35195.035195.00.0000000006200.00.000006012710.765556
9997200.00.00.00.0040444436.000000NaN4740.04740.00.0000000001300.00.01079480474021.708333
99973456710.020.00.00.70507824NaNNaN1200.01200.0NaN000000NaNNaN180174471962731002.185278
99974561020.00.00.00.0641753117.500000NaN15000.015000.00.0000000002100.00.0101833956180347859.725278
9997500.00.00.0NaN4134.66666737.50000013246.014817.00.0000000002210.00.010003060211.585278